Method of providing online incentives

ABSTRACT

A method of providing incentives to bidders on an auction item using a database and a system, the method including creating the consumer profile for at least one consumer, choosing at least one of the unsold items from the inventory based on the consumer information in the consumer profile, generating the incentive for the chosen unsold item based on the consumer file, the selection of consumer information of the incentive being substantially similar to the consumer information in the consumer profile, and offering the chosen unsold item and the incentive to the at least one consumer to induce purchasing of the chosen unsold item. The database has an inventory file having at least one of detailed descriptions of an inventory, a list of similar items of the inventory, and a list of complementary items of the inventory, and a consumer profile having at least one of bidding history, start bid, bid frequency, bid increment, final bid, winning bid, target product, Internet service provider, zip code, credit card type, and coupon redemption rate. The system has a first memory for storing consumer profiles having consumer information, including at least information relating to bids on the auction items, a second memory for storing unsold items in an inventory, and a third memory for storing a plurality of incentives for each unsold item, each incentive having a value based on a selection of the consumer information.

RELATED APPLICATION

This patent application is a continuation of copending U.S. patentapplication Ser. No. 09/711,183, entitled “METHOD OF PROVIDING ONLINEINCENTIVES”, which was filed on Nov. 13, 2000. The disclosure of theabove application is incorporated by reference herein in its entirety.

FIELD OF INVENTION

The present invention relates to consumer incentive systems, and inparticular, relates to online incentives for auction bidders.

BACKGROUND OF INVENTION

One method of selling merchandise may be through online auctions.Consumers and retailers may sell merchandise over the Internet wherewinners may be determined by method of the auction, bid price, bidquantity and bid date. While many consumers may bid on goods at a website, those bidders willing to purchase merchandise, but havingunsuccessful bids, may not purchase any merchandise. It is believed thatthis causes a loss of potential revenue for sellers having additionalinventory available for sale at a price at or below the consumer'sreservation price.

It is believed that online dynamic pricing tools are increasingly beingused to help move slow-moving or excess inventory faster and at a moreattractive price than was possible before the advent of the Internet.Applicants believe that today's online auctions and buying groups,however, do not always create an optimal selling environment. In theperfect world of pricing theory, where buyers and sellers have aninfinite amount of time to wait, where product value does not decay overtime, and where there are no explicit inventory holding costs,Applicants believe that auctions may be the best inventory dispositionoption for the seller. In the real world of retail merchandising,however, Applicants believe that these perfect conditions crumble,demanding a more creative approach to selling than simple auctions.

Applicants believe that retailers currently have three principal optionsfor dealing with excess inventory. First, they may accumulate bulkquantities to be sold to liquidators (often for pennies on the dollar)at the end of the product's life cycle. Second, they may advertise aproduct clearance sale to the general population. Third, they mayaccumulate bulk quantities and ship the merchandise back to themanufacturer for credit. It is believed that all of these methods reducethe value that retailers are able to capture from these sales. Bulksales to liquidators, as with any transaction involving a middleman, mayresult in a transfer of economic value from retailers to liquidators.General price reductions through clearance-style sales may transfer toomuch economic value from retailers to consumers, as many consumers whomay have bought merchandise at a price higher than the promotion saleprice in the absence of a sale may instead divert to slow-movinginventory at a lower price point.

Applicants believe that to help solve the problem of lost revenue atauctions and reduce the cost of holding excess inventory, there are manydifferent incentive and award programs to influence consumers topurchase on-line. For example, it is believed that one incentive programallows users to earn points, which are redeemable for products, byreading e-mail offers, shopping, completing surveys, accepting trialoffers, referring other users, and reviewing web sites. It is believedthat another incentive program allocates monetary amounts available forexpenditure through credit instruments issued to program participantswhen the participants perform to a designated level of achievement.Applicants believe that these systems are generally offered by a singlesponsor and are generally limited to offering consumers the ability toparticipate in incentive programs. It is also believed that the systemsare typically not applicable for activity on auction sites.

SUMMARY OF THE INVENTION

The present invention provides a method of providing incentives tobidders on an auction item, including creating a consumer profile for atleast one consumer, the consumer profile including at least informationrelating to a bid on the auction item, choosing at least one unsold itemfrom an inventory based on the information in the consumer profile,generating an incentive for the at least one chosen unsold item based onthe consumer profile, and offering the at least one chosen unsold itemand the incentive to the at least one consumer to induce purchasing ofthe chosen unsold item.

The present invention also provides a system for implementing anincentive program for bidders on auction items, including a consumerdatabase storing consumer information, software for choosing unsolditems from an inventory and generating incentives for the chosen unsolditems based on the consumer information in the consumer database, andsoftware for offering the chosen unsold items and the incentives toconsumers to induce purchasing of the chosen unsold items. The consumerinformation has, at the least, information relating to bids on theauction items.

The present invention also provides a system for an incentive programfor bidders on auction items, including a first memory for storingconsumer profiles having consumer information, having at leastinformation relating to bids on the auction items, a second memory forstoring unsold items in an inventory, and a third memory for storing aplurality of incentives for each unsold item, each incentive having avalue based on a selection of the consumer information.

The present invention also provides a method of providing incentives tobidders on an auction item using a system having a first memory forstoring consumer profiles that have consumer information including atleast information relating to bids on the auction items, a second memoryfor storing unsold items in an inventory, and a third memory for storinga plurality of incentives for each unsold item, wherein each incentivehas a value based on a selection of the consumer information. The methodincludes creating the consumer profile for at least one consumer,choosing at least one of the unsold items from the inventory based onthe consumer information in the consumer profile, generating theincentive for the chosen unsold item based on the consumer profile, theselection of consumer information of the incentive being substantiallysimilar to the consumer information in the consumer profile, andoffering the chosen unsold item and the incentive to the at least oneconsumer to induce purchasing of the chosen unsold item.

The present invention further provides a database for an incentiveprogram for bidders on auction items, including an inventory file havingat least one of detailed descriptions of an inventory, a list of similaritems of the inventory, and a list of complementary items of theinventory, and a consumer profile having at least one of biddinghistory, start bid, bid frequency, bid increment, final bid, winningbid, target product, Internet service provider, zip code, credit cardtype, and coupon redemption rate.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, illustrate the presently preferredembodiment of the invention, and, together with the general descriptiongiven above and the detailed description given below, serve to explainthe features of the invention.

FIG. 1 is a block diagram of the method of the preferred embodiment

FIG. 2 is a block diagram of the preferred embodiment of the method ofFIG. 1 incorporated into an overall product sales system.

FIG. 3 is a block diagram of the classify consumer step of the method ofFIG. 2.

FIG. 4 is a bar graph of incentive options of the method of FIG. 2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

As shown in FIG. 1, the method, or incentive system 20, includescreating a consumer profile 16 for at least one consumer, choosing atleast one unsold item, or product to offer, 17 from an inventory basedon the information in the consumer profile, generating, or calculating,an incentive for the chosen unsold item 24 based on the consumerprofile, and offering the chosen unsold item and the incentive to theconsumer 25 to induce purchasing of the chosen unsold item. The consumerprofile includes at least information relating to a bid on the auctionitem.

In the preferred embodiment, as shown in FIG. 2, the incentive system 20is incorporated to an overall product sales system with a data providersystem 10 and a product supplier system 30. In the preferred embodiment,the data provider system 10 is an auction house and the product supplier30 is a retailer and/or manufacturer. It should be understood that thedata provider system 10 may be any system that supplies informationabout consumers, such as retailers, distributors, and manufacturers, andthe product supplier system 30 may be any system that supplies goods orservices, such as financial services and real estate brokers.

In the preferred embodiment, the consumer logs onto the Internet 1 andchooses an auction site that is entered and viewed 2. It should beunderstood that the device for connection to the Internet 1 could be apersonal computer implementing a web browser with a graphics userinterface (GUI). However, other connection devices, such as telephoneswith a display to communicate information with or without graphics,personal display devices, or any other device that allows communicationlinks to and from the Internet, may be used. Moreover, although Internetcommunication protocol, such as TCP/IP protocol is a preferredcommunication method for the preferred embodiment, other similar methodsmay be employed. In addition, in particular applications of thepreferred embodiment, the process of viewing may be conducted via aconnection, such as a dial-up or direct connection link. For example,secured connections may be used to provide incentives on financialinstruments or other secured items.

In the preferred embodiment, the consumer bids on items through theInternet. In an alternative embodiment, the consumer attends an auctionhouse, in person, and bids on auction items, using a method required bythe auction house. This method may include raising a bidding card with anumber assigned to the consumer. In the preferred embodiment, as theconsumer explores the site and places bids on auction items, the auctionhouse records the consumer's browsing behavior 3, bidding behavior 4,and other data 5. The browsing behavior 3 may include the auction itemsthe consumer has considered and the bidding behavior 4 may include startbids and bid frequency. The other data 5 may include information aboutthe consumer's personal computer, such as Internet service provider andmodem speed. The recorded information is stored in a data file 6 by theauction house. The auction house continuously monitors all bidders andrecords their behavior, while the auction is still open or there isstill time remaining 7, specified by yes 7 a. When there is no timeremaining 7, specified by no 8 a, the auction is closed 8 and the datafile 6 is then sent 9 to the incentive system 20.

This incentive system 20 may use existing auction infrastructures tocapture and analyze relevant data in the data file 6. The system 20enables retailers and manufacturers to minimize cash flow constraints,brand dilution concerns, inventory levels, fragmentation of the onlinedynamic pricing market, and impatient bidder populations, which allconspire to create conditions that reduce the viability of simpledynamic pricing formulas.

The sent data received by the incentive system 20, preferably, hasprice-sensitivity indicators for all bidders, including browsingbehavior 11, bidding and behavior values 12, personal computerinformation 13, such as an Internet service provider and modem speed,and transaction history 14. Preferably, the browsing behavior 11 hasclick stream information, which includes other components of the auctionsite that were visited by the consumer, number of pages visited, timespent on each page during each visit per each auction, number ofauctions visited/participated, and frequency of revisiting auctions.Preferably, the bidding and behavior values 12 includes bidding history,start bid, bid frequency, bid increment, final bid, winning bid, targetproduct, coupon redemption rate, and keywords used in auction searches.The personal computer information 13 includes Internet service provider,referring URL link, web browser make and model, operating system,bookmarks, zip code, access location (work or home), and credit cardtype. Preferably, the bidding and behavior values 12 also containwhether the consumer's bid was a successful bid, which is the winningbid, or an unsuccessful bid. Demographics, psychographics, marketconditions, and any other relevant information from other databases 15or any other relevant information identified through contact with theconsumer may also be recorded. From this information 11, 12, 13, 14 and15, the system 20 creates a consumer profile 16.

The information in the consumer profile is used and updated throughoutthe system 20. One such use is in classifying, or categorizing, theconsumer into a consumer category 18. As shown in FIG. 3, an initial, orcurrent, consumer profile, which contains the information 11, 12, 13,14, and 15 that is unique to the consumer, is inputted 16 a. In creatingan updated consumer profile 16 b, which includes a classification, theinitial consumer profile is compared with existing consumerclassifications 19, or index of consumer behavior indicators, in adatabase of the system 20. There are several different classifications,which may be modified or divided into more classifications, as requiredby the system. In the preferred embodiment, classification categoriesinclude at least:

Price Sensitive/Insensitive

-   Based on recorded behavior, a consumer may be classified as either    being price sensitive or insensitive. For example, a consumer who    has been found to be bidding in three auctions, but consistently    stops bidding once the price for the item reaches some set level is    in the price sensitive category.    Brand Sensitive/Insensitive-   Based on recorded behavior, a consumer may be classified as either    being brand sensitive or insensitive. For example, a consumer who is    bidding on a given item from brand X and has also looked at/bid on    brands Y, Z, A, B, and C may be labeled as brand insensitive.    Time Sensitive/Insensitive-   Based on recorded behavior, a consumer may be classified as either    being time sensitive or insensitive. For example, a consumer who    enters a nine day auction on the ninth day with 3 hours remaining to    make his/her first bid may be labeled as time sensitive.    Feature Sensitive/Insensitive-   Based on recorded behavior, a consumer may be classified as either    being feature sensitive or insensitive. For example, a consumer    views/bids only on items with feature X would be considered feature    sensitive.    Intersection(s) of Above Listed Classifications:-   Classifications may also be combined to form new classifications,    which may be improved groupings. For example, if the price sensitive    and brand insensitive categories intersect, then the resulting    classification is a consumer that views/bids on items that focus    on/around a given price point, but are from numerous different    brands.

The comparison determines if there is a match 21 between the consumerprofile and a classification in the database. If there is a matchbetween the consumer profile and a classification, specified by yes 21a, between the consumer profile and the classification, then theconsumer profile 16 b is categorized in that classification. If there isnot a match between the consumer profile and the classification,specified by no 21 b, then a new classification will be created 22 andthe database will be updated with the new classification. The consumerprofile 16 a will again be compared 19 to the classifications in thedatabase and a match will be found, specified by yes 21 a, because theconsumer profile 16 a will match the classification created with theconsumer profile. As the classification is stored in the consumerprofile, any personal information unique to the consumer, such as namesand credit card numbers, will be deleted. The consumer profile 16 willthen have the consumer information from 16 a, less any personalinformation, and the classification from 16 b.

The system 20 also includes choosing at least one unsold item from aninventory 17. In order to choose the unsold items from inventory 17,inventory positions of retailers and/or manufacturers are monitored 31.This entails collecting inventory data from the retailers and/ormanufacturers 33. The inventory data 33 may include detaileddescriptions, such as inventory type, inventory levels, quantity andphysical and virtual location of inventory, lists of similar items, andlists of complementary items. An inventory database system 35 is createdand updated from the inventory data 33 of both the retailers andmanufacturers. A maximum allowable discount from the full price for eachunsold item in the inventory, or minimum price, is also determined andstored in the inventory database system 35. The maximum allowablediscount may be negotiated and is based on the seller's, or retailer'sor manufacturer's, inventory pressure, which is a measure of the desireto more quickly move the inventory out of their stores and to anotherretailer or distributor or a consumer. Pressure is driven by theexplicit and opportunity costs of holding onto the inventory, as well asspace and any other constraints.

Preferably, the system 20 uses the inventory database system 35 toenable retailers struggling with the issue of excess inventory toeffectively and profitably move excess inventory at prices that meettheir margin objectives and on a schedule that meets their cash flowobjectives. Preferably, the inventory database system 35 are received bythe system 20, where seller's inventory positions 36 containing theinventory data 33, and incentive options 37 are determined. The seller'sinventory positions 36, preferably, contain products in theirslow-moving or excess inventory and dead inventory and returned items toenable the sellers to effectively and profitably move excess inventoryat prices that meet their margin objectives, and on a schedule thatmeets their cash flow objectives. The seller's forecasting andpoint-of-sale inventory control systems may also be considered. Theincentive options 37 include additional percentage or specified amountdiscounts from manufacturers to help the seller or retailer move theinventory, such as where the retailer gives an additional 5% incentiveon all Model 1234 goods or services moved out of inventory, and freeadd-ons, such as more warranty or free or otherwise discountedadditional goods or services.

Based on the product attributes the consumer most prefers, which arerevealed in the browsing behavior 11 in the consumer profile, and theseller's inventory position 36, an attribute based product considerationset, which enables the system 20 to choose unsold items from inventory17, is created. For example, if the consumer is considered brand-driven,where the consumer looks for products across the price spectrum, butonly focuses on a given manufacturer, the products in the productconsideration set will most likely be products by that givenmanufacturer. The products offered will be determined by the seller'sinventory position 36 because only certain products will be available tooffer from the seller's inventory. Similarly, if the consumer isconsidered price-sensitive, where the consumer looked for productswithin a certain price range, then only products within that price rangemay be included in the product consideration set.

When the consumer profile is classified 18, the unsold item frominventory is chosen 17, and the seller's inventory position 36 andincentive options 37 are determined, the system 20 will generateincentives 24 using a learning model. The learning model calculates theincentives based on the information in the consumer profile,classification, product consideration set, seller's inventory position36, and incentive options 37. Preferably, the calculations performed bythe learning model will maximize the seller surplus 43. The learningmodel is constantly monitored and updated to improve accuracy. When thesystem 20 is turned on at Day 0, it has minimal information on which tobase incentive generation decisions. As time goes by and redemption datais accumulated, statistics will be used in the learning model todetermine what information, or attributes, add to the ability toaccurately produce incentives and which attributes do not add value.Once this is known, attribute weights will be adjusted to reflect thisinformation. Over time, it is possible to more accurately understand howimportant each attribute is in computing the proper incentive. In thepreferred embodiment, the redemption rate is among the primaryindicators monitored and updated in order to improve the accuracy of theattributes, of the data, and their weightings in the learning model.Each profile/incentive combination may be regularly monitored foraccuracy by reviewing redemption statistics. For example, ConsumerProfile #A3421 may suggest a given amount of incentive. If thisincentive is given to the consumer whose behavior matches the profileand the consumer chooses the incentive, then the score for theprofile/incentive combination will improve. Consumer profiles exhibitinglow or split accuracies will either be recomputed (if low) or split intofurther profiles (if split) to improve their accuracy.

In regard to the information in the consumer profile, preferably, eachpiece of data is assigned a weight, which determines the amount ofinfluence, or importance, each piece of data will have in thecalculation of the incentive. Preferred attributes include intensity(how often the consumer bid, checked on the current bid price, viewedthe website in general), competitiveness (if the consumer responded eachand every time he or she was outbid), final bid-price (as a percentageor full-retail price of an item, where the higher the final bid, theless of an incentive a consumer will receive), and zip code drivendemographics (higher level annual income zip-codes will receive less ofa discount than lower annual income zip codes). In one embodiment, thenumber of bids may be used by the learning model, where if consumer 1(C1) bid 5 times during the auction and consumer 2 (C2) bid 10 timesduring the auction, it may be determined that C2 is more interested inthe auction item than C1. As a result, C1's incentive will be less thanC2's incentive because C2 is more interested and will be willing to paymore for a given item. In another embodiment, the referring URL link maybe used by the learning model, where C1 enters the auction from apricing/search bot, which is a class of Internet search agents employedby consumers to scour the various retailers and auctions databases tolook for an item with the criteria the consumer specifies, such asprice, make, model, etc., and C2 enters from some generic link (startpage of C2's Internet Service Provider). C1 will receive incentivesgreater than C2 because entering through a price-bot indicates that C1is price sensitive. In yet another embodiment, the learning model mayuse current and past bid data and number of auctions visited and/orparticipated, where C1 participated in three auctions from the startpoint of each auction, but consistently dropped out when the price ofthe auction item reached X% of full retail or some actual dollar amountand C2 participated in only one auction and joined with a few hoursremaining. C1 may be viewed as more price sensitive than C2 because C1has a clear price ceiling, so C1 will receive an incentive greater thanC2.

In regard to the classifications, consumers determined to be pricesensitive may receive incentives with larger values than thosedetermined to be price insensitive, consumer's determined to be brandsensitive may receive incentives targeted only for specific brands theyare determined to be interested in while consumers determined to bebrand insensitive may receive incentives for any number of brandsavailable, and consumer's determined to be time sensitive may receiveincentives with lower values than those consumers determined to be timeinsensitive. Additionally, consumers determined to be feature sensitivemay only receive incentives for items containing the feature ofinterest, or in the case where items containing the feature of interestare not available, the incentive for an alternate item will be greaterthan if an item with the feature was available.

The learning model will also determine the products that meet thecriteria of both the product consideration set and the seller'sinventory position 36. The learning model calculates incentives forproducts determined to be part of an individual consumer's productconsideration set. The product consideration set may be constrained intwo ways. First, the set may be constrained by the available inventoryfrom the seller's or retailer's shelves. Second, the set may be chosenbased on the behavior of the individual consumers. The products offeredmay be comparable or different items in relation to the original auctionitem, and all of the products from the consideration set may be offeredor a select few may be offered. The products may also be from one ormore sellers. For example, if consumer 1 (C1) had been viewing goods A,B, C, D, and E, all from different manufacturers, but with retail pricesfalling between $300-$325, then C1 would be offered (based onavailability) goods from any manufacturers with similar average prices.In contrast, if consumer 2 (C2) had been viewing goods V, W, X, Y, andZ, all from manufacturer J, but varying in price from $200-$500, C2would be offered (based on availability) goods from manufacturer J thatvaried in price. Preferably, each of these goods will have a discountzone 38, where the effective price 41 will be determined based on theconsumer profile and matched classification.

FIG. 4 displays a bar graph of the incentives that may be calculated forGoods A, B and C. The incentives are calculated by first establishing adiscount zone 38 between a full price 39 of the unsold item and theminimum price 40, which is the full price 39 less the maximum allowablediscount. More specifically, an effective price 41 within the discountzone 38 deemed necessary to trigger a consumer purchase is calculatedand offered to the consumer as the incentive. The effective price 41will vary within the discount zone and a larger discount zone 38 willresult in a greater variance. For example, the discount zone 38 of GoodC is greater than the discount zone 38 of Goods A and B, so there ismore variance in the effective price 41 for Good C than Goods A and B.The discount zone 38 may be divided into a consumer surplus 42 betweenthe full price 39 and the effective price 41 and a seller surplus 43between the effective price 41 and the full price 39 less the maximumallowable discount 38. An effective price 41 that is closer to the fullprice than the minimum price 40, as shown for Good C, will result in agreater seller surplus 43. The effective price 41 will be the incentiveoffered to the consumer.

When an incentive is generated 24, or created, the system 20 will thenoffer the incentive 25 to the consumer. In the preferred embodiment, anincentive notification is created and sent to the consumer. Theincentive notification may be in the form of e-mail or any other type ofdelivery. The incentive may be a general incentive for any product or aspecific incentive for a specific product. In addition, the notificationmay contain one or more incentives offered at the same time to the sameconsumer. These incentives may be based on a retail price of the auctionitem or an unsuccessful bid, which is less than a lowest successful bidin the auction. The incentives may also be in the form of a coupon,discount, rebate, additional product, reward, or any other type ofoffer. For example, an e-mail entitled “Manager's Special” may be sentto C1, a consumer, with notification that C1 was outbid on a S brandproduct. The e-mail may or may not give C1 a time limit to respond tothe offers, or incentives. There may be several incentives for C1 tochoose, such as a coupon for $91 off a J brand product A with a MSRP(manufacturer's suggested retail price) of $299.95 from Stereo Store, acoupon for $71 off a P brand product A with a MSRP of $349.99 fromElectronic Retailer, a coupon for $46 off a S brand product A with aMSRP of $299.99 from Ed's Manufacturer, and a coupon for a S brandproduct B with a MSRP of $249.99 from Stereo Store. The J brand, Pbrand, and S brand products A are comparable items to the S brandproduct A in which C1 was outbid, whereas the S brand product B is adifferent item, but is the same brand as the auction item. In addition,the products are from three different sellers, with two products fromthe same seller. The incentives are also all based on the retail priceof the product.

When the incentive is delivered to the consumer, the consumer has theoption of selecting the incentive 27. If the consumer chooses not toselect the incentive, specified by no 28, the system 20 will update 50the consumer profile with information that the incentive was notredeemed. The consumer may specify no 28 by not responding to theincentive within a specified period of time, terminating communications,or responding with a rejection. When no 28 is specified, the contactwith the consumer will end 51.

If, however, the consumer chooses to select the incentive, specified byyes 29, the consumer profile will be updated 50 with information thatthe incentive was redeemed and the redemption will be processed 52. Theredemption information is then used to determine how accurate thelearning model is performing. For example, if approximately 50% of theconsumers in a classification expected to act on an incentive do not,the learning model may then split the classification into two otherand/or new classifications. The data elements that are familiar to thosechoosing the incentive will form a first split classification and thedata elements that are familiar to those not choosing the incentive willform a second split classification. If, on the other hand, theredemption rate is statistically very low, the learning model mayrecompute, or modify, the classification altogether.

To accept the incentive, preferably, the consumer may click on ahypertext link, which may be in the form of an image, and proceed withpurchasing the product. This allows for automated fulfillment ofrewards. Processing 52 includes collecting payment information, such asa credit card number, and shipping or pick-up information. The consumeror retailer may choose to arrange shipment of the product if the storeis not located near the consumer or the consumer may choose to pick upthe product at a local retailer. At this point, a commission forproviding the incentive generated by the system 20 will be received bythe operator of the system 20. Preferably, the commission is atransaction fee or a percentage of the seller surplus 42 on the productoffered with the incentive.

When the payment information is collected from the consumer, it isforwarded in the form of a pick and pack list and received 53, alongwith payment, by the seller. The seller will pull the item and/orassemble shipment 54. If the consumer specified an in store pick-up 55,specified by yes 55 a during processing 52, then the seller will holdonto the offered product until the consumer goes to the store forpick-up 56. On the other hand, if the consumer did not want in storepick-up 55, specified by no 55 b, then a carrier will receive paymentfor shipping and the shipping information 57. Preferably, the carrier,or courier, will arrive at the retailer, or seller, to pick up theoffered product and apply a shipping label 58. The offered product, oritem, will be picked up 59 and delivered 60 to the consumer. The offeredproduct, in the form of a package, will then be received by the consumervia the courier 61.

The system 20 also includes a consumer database storing consumerinformation, software for choosing the unsold items from the inventoryand generating the incentives for the chosen unsold items based on theconsumer information in the consumer database, and software for offeringthe chosen unsold items and the incentives to consumers to inducepurchasing of the chosen unsold items. The consumer information has, atthe least, information relating to bids on the auction items.

The system 20 also includes a first memory for storing the consumerprofiles having consumer information, having at least informationrelating to bids on the auction items, a second memory for storing theunsold items in the inventory, and a third memory for storing theincentives for each unsold item, each incentive having a value based ona selection of the consumer information. The first memory storescomputed historical, current, and projected aggregated consumerinformation as consumer profiles.

While the invention has been disclosed with reference to certainpreferred embodiments, numerous modifications, alterations, and changesto the described embodiments are possible without departing from thesphere and scope of the invention, as defined in the appended claims andtheir equivalents thereof. Accordingly, it is intended that theinvention not be limited to the described embodiments, but that it havethe full scope defined by the language of the following claims.

1. A system for an incentive program for bidders on auction itemsoffered via a computer network, the system comprising: a firstelectronic memory for storing via a computer network consumer profileshaving consumer information, including at least information relating tobids on the auction items offered via a computer network; a secondelectronic memory for storing unsold items in an inventory; a learningmodel that calculates an incentive having a value based upon informationrelating to the bid on the auction item; and a third electronic memoryfor storing a plurality of incentives for each unsold item, eachincentive having a value based on a selection of the consumerinformation.
 2. A database stored in an electronic memory for anincentive program for bidders on auction items offered via a computernetwork comprising: an inventory file having at least one of detaileddescriptions of an inventory of auction items offered via a computernetwork, a list of similar items of the inventory, and a list ofcomplementary items of the inventory; and a consumer profile having atleast one of bidding history, start bid, bid frequency, bid increment,final bid, winning bid, target product, Internet service provider, zipcode, credit card type, and coupon redemption rate acquired throughknowledge of previous bids made on auction items offered via a computernetwork.